© 1999 American Heart Association, Inc.
Original Contributions |
Relationship of Plasmin Generation to Cardiovascular Disease Risk Factors in Elderly Men and Women
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Abstract |
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Abstract—Plasmin–
2-antiplasmin
complex (PAP) marks plasmin generation and fibrinolytic balance. We
recently observed that elevated levels of PAP predict acute myocardial
infarction in the elderly, yet little is known about the correlates of
PAP. We measured PAP in 800 elderly subjects who were free of clinical
cardiovascular disease in 2 cohort studies: the
Cardiovascular Health Study and the Honolulu Heart
Program. Median PAP levels did not differ between the
Cardiovascular Health Study (6.05±1.46 nmol/L) and the
Honolulu Heart Program (6.11±1.44 nmol/L), and correlates of PAP were
similar in both cohorts. In CHS, PAP levels increased with age
(r=0.30), procoagulant factors (eg, factor VIIc,
r=0.15), thrombin activity (prothrombin fragment F1+2,
r=0.29), and inflammation-sensitive proteins (eg,
fibrinogen, r=0.44; factor VIIIc,
r=0.37). PAP was associated with increased
atherosclerosis as measured by the ankle-arm index
(AAI) (P for trend, 
0.001). PAP was negatively related
to factors associated with the insulin resistance syndrome (IRS) (eg,
fasting insulin, r=-0.26; body mass index,
r=-0.26), possibly reflecting an association with
plasminogen activator inhibitor-1
(r=-0.29). Although our study did not have sufficient
power to detect a significant interaction, PAP and AAI appeared to be
more weakly associated in subjects with more manifestations of the IRS:
PAP appeared more strongly associated with AAI in the subgroup with 0
or 1 metabolic disorders (P0.001; slope
estimate, -0.14) compared with the subgroup with 2 or more
metabolic disorders (P=0.10; slope estimate,
-0.08) and in those with non–insulin-dependent diabetes mellitus
(P=0.46; slope estimate, -0.04). Although PAP reflects
reactive fibrinolysis and is associated with
subclinical atherosclerosis, this relationship may be
weaker in populations with characteristics of the IRS, possibly
reflecting the inhibitory effects of
plasminogen activator inhibitor-1
on PAP. Decreased fibrinolysis in the presence of
subclinical disease in subjects with
hyperinsulinemia or glucose intolerance is
consistent with the premise that depressed plasmin generation
may enhance the progression of atherosclerosis in
these people.
Key Words: blood coagulation • fibrinolysis • myocardial infarction • elderly • diabetes
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Introduction |
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Markers of both increased and decreased fibrinolytic activity1 2 3 have been associated with an increased risk of cardiovascular disease (CVD). Plasminogen activator inhibitor-1 (PAI-1), the major negative regulator of fibrinolysis, is a risk factor for recurrent myocardial infarction (MI)4 and may mediate the increased CVD risk associated with the insulin resistance syndrome (IRS).5 Paradoxically, we and others have shown that higher levels of D-dimer, a product of increased fibrinolysis, predict incident CVD in middle-aged men6 and elderly populations.7 It is therefore important to gain precise knowledge about the process of fibrinolysis with respect to CVD risk.
Plasmin is the enzyme responsible for
fibrinolysis.8 Its production is
accelerated by the presence of fibrin and inhibited by PAI-1. Free
plasmin is rapidly inhibited by 2-antiplasmin, and the resulting
plasmin–2-antiplasmin complex (PAP) marks plasmin generation, and
thus, fibrinolysis. Elevated levels of PAP are
associated with the incidence of acute MI in the elderly,9
but to our knowledge, there are no data on the correlates of PAP in the
general population.
Because 
80% of fatal MIs occur in older persons,10
studies of the elderly are needed. The Cardiovascular
Health Study (CHS) is a cohort study of community-dwelling persons over
the age of 65 years.11 Similarly, the Honolulu Heart
Project (HHP), in its current form, is a longitudinal
population-based study of elderly Japanese-American men.12
We measured PAP levels in subgroups of the CHS (n=400) and the HHP
(n=400) who were free of clinical CVD to limit the influence of
clinical disease on measurements. We report on the cross-sectional
correlates of this fibrinolysis marker with respect to
traditional and novel CVD risk factors, such as inflammation, and
measures of subclinical disease.
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Methods |
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Population
The definitions of disease and risk factors in the HHP are identical to those in the CHS unless otherwise noted. The CHS cohort included 5201 men and women
65 years.13 Baseline
examinations were performed over 1 year beginning in May 1989. These
included a medical history, physical examination, and phlebotomy.
Subjects were classified at baseline according to the presence or
absence of previous clinical CVD.11 Carotid
ultrasound,14 echocardiogram measurements,15
blood pressure including the ankle-arm index (AAI),16 and
12-lead resting ECG were performed to assess subclinical
atherosclerosis. The original HHP cohort was enrolled from 1965 to 1968 and included 8006 Japanese-American men between the ages of 45 and 68 years living on the island of Oahu, Hawaii.17 A total of 3741 men aged 71 to 93 years old participated in the fourth examination (1991 to 1993), which included a medical history questionnaire, physical examination, blood collection, and classification of clinical CVD status.12
A subset of 400 individuals free of baseline clinical CVD was selected from each cohort. The CHS group was evenly divided by sex and among 5 age strata: 65 to 69, 70 to 74, 75 to 79, 80 to 84, and 85+ years. The subset of the HHP cohort included men divided evenly among 4 age strata: 71 to 74, 75 to 79, 80 to 84, and 85+ years.
Blood collection and assay work were completed using the same methods for both studies. Blood specimens from both cohorts were analyzed at the Laboratory for Clinical Biochemistry Research at the University of Vermont, Colchester.18 All participants gave informed consent, and all work was done under institutionally approved protocols.
Definitions
Baseline CVD included MI, angina or use of
nitroglycerin, coronary angioplasty,
coronary artery bypass surgery, stroke, transient
ischemic attack, carotid endarterectomy,
intermittent claudication, or a history of peripheral
arterial angioplasty or bypass surgery.11
Hypertension was defined as seated systolic blood pressure
160 mm Hg, diastolic pressure 95 mm Hg, or
self-reported high blood pressure and use of antihypertensive
medication. Obesity was defined as weight >130% of ideal body mass by
using the body mass index (BMI; weight
[kg]/height2 [m2]).
Abnormal glucose tolerance was defined as "impaired" (fasting
glucose <140 mg/dL and a 2-hour postglucose challenge value between
140 and 199 mg/dL) or "diabetes" (fasting glucose >140 mg/dL,
glucose >200 mg/dL 2 hours after a glucose load, or use of insulin or
oral hypoglycemic agents, based on World Health Organization criteria;
use of American Diabetes Association criteria was not in place at the
time of data analysis for this project).
Dyslipidemia was defined as either a low HDL
cholesterol (HDL-C) level (<35 mg/dL for men, <45 mg/dL
for women19 ) or a high triglyceride level
(>200 mg/dL).
The term "insulin resistance syndrome" (IRS) has been used to describe the clustering of metabolic disorders20 associated with later onset of non–insulin-dependent diabetes mellitus (NIDDM).21 22 In the CHS subgroup, we adopted recently proposed criteria for estimating the metabolic clustering associated with the IRS19 by summing the number of the following disorders for each subject: dyslipidemia, hypertension, and glucose intolerance. We also included obesity as a metabolic disorder. The stepwise increase in the number of disorders has been proposed to indicate decreasing insulin sensitivity.19 Diabetics were analyzed separately as the "worst case" of IRS.21 22
Subclinical carotid atherosclerosis in the CHS was described using the maximum percent diameter stenosis in the left or right internal carotid artery or the mean of multiple measurements of maximal intimal-medial thickness of the common and internal carotid arteries.14 Data on subclinical atherosclerosis of the carotid arteries were not obtained in the HHP cohort.
Blood Collection and Analysis
The CHS blood collection and analysis methods have been
reported.18 Blood was collected in a fasting state, and a
special tube designed to prevent in vitro clotting activation was used
for most immunoassays.23 This tube (SCAT-1, Hematologic
Technologies, Inc) contained, in whole blood, 4.5 mmol/L
EDTA, 0.15 KIU/L aprotinin, and 20 mol/L
D-Phe-Pro-Arg-chloromethylketone.
Citrated plasma was used for functional assays. Fasting HHP blood
samples were collected using identical methods; however, many of the
specialized coagulation and inflammation assays described below were
available only on CHS samples.
The fibrin fragment D-dimer was measured by ELISA as developed by Collen DeClerck and colleagues,24 who kindly provided reagents for this and all other fibrinolytic immunoassays. The coefficient of variation (CV) was 7.0%. PAP was measured by ELISA.25 The CV was 3%. Plasminogen was measured by rate chromogenic assay, with a CV of 3.6%. PAI-1 antigen was measured by ELISA with a CV of 8.4%.26 27 Tissue plasminogen activator antigen28 and tissue plasminogen activator/PAI complex were measured with ELISAs, with CVs of 7.0% and 14.3%, respectively.
C-reactive protein (CRP)29 was measured by ELISA (antibodies and antigens from Calbiochem) with a CV of 8.9%. Apolipoprotein(a) was measured by ELISA (reagents provided as a gift from Dr Wai-Li Wong, Genentech, Inc, South San Francisco, Calif), with a CV of 7.5%.30 Factor IXc and factor Xc were measured in plasma by using 1-stage clot-rate assays and the Diagnostica Stago ST4 instrument according to the manufacturer's recommendations,31 with CVs of 5.8% and 4.7%, respectively. Fibrinogen and factors VIIc and VIIIc were measured using citrated plasma; lipid and general blood chemistry levels were measured using 4.5 mmol/L EDTA–plasma and –serum, respectively, as described.18 The enzymatic lipid methods for total cholesterol, HDL-C, and triglycerides were performed under certification from the Centers for Disease Control and Prevention, Atlanta, Ga.
Statistical Analysis
Identical strategies were used to analyze the 2 data
sets. PAP was skewed toward the right, and thus, these values were
natural log transformed for most analyses. In the CHS, 4
warfarin users and 1 person with a missing PAP level were excluded
(n=395). Because the HHP data set consisted only of men 71 years
(n=400), women (n=199) and men <70 years (n=38) were removed from the
CHS data set along with a small number of nonwhite men (n=6) for
comparison of PAP correlates between the CHS and the HHP. Bivariate
associations for continuous variables were determined by Pearson
correlation coefficients, and associations with categorical
variables were determined by ANOVA. Because of the large number of
analyses performed, the level of significance was defined as
P0.01. A formal race-interaction term was determined by
linear regression for each variable common to both cohorts.
We used linear regression on PAP to explore confounding between PAI-1
and factors associated with the IRS: BMI, HDL-C,
triglycerides, glucose levels, and insulin levels. We
examined the relationship of AAI, as a measure of subclinical
atherosclerosis, with PAP by the number of
metabolic disorders (see definitions). Of the nondiabetics,
119 had 0 disorders; 109, 1 disorder; 62, 2 disorders; 29, 3 disorders;
and 1 participant had 4 disorders. For analysis we established
3 groups: 1 disorder (n=228), 2 disorders (n=92), and diabetes
(n=73).
In multivariable linear regression models, AAI and measures of
insulin resistance (BMI, dyslipidemia,
hyperinsulinemia, and glucose intolerance) were
entered initially, followed by variables significantly related to
PAP in bivariate correlations. Because we believe that
atherosclerosis is strongly related to inflammation
(fibrinogen, factor VIIIc, and CRP) and procoagulation (F1+2 and
fibrinopeptide A), markers of these processes were not
allowed to replace atherosclerosis in our model.
Because PAP and D-dimer are logically closely correlated, we excluded
D-dimer from the model for PAP. The predicted difference in PAP for a
specified change equivalent to 1SD of each independent variable was
determined using models without logarithmic transformation of the
specified independent variable. The level of significance for
multivariable modeling was set at P0.05.
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Results |
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Distribution of PAP
The characteristics of the CHS subcohort have been described32 and are generally similar to those of the CHS subgroup of men only (Table 1
).
Compared with the HHP cohort, the CHS subgroup of white men 70 was
older, had higher levels of fibrinogen and CRP, and included a greater
percentage of subjects with an abnormal AAI. The HHP subgroup had a
higher frequency of hypertension, diabetes, and current smokers.
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PAP levels ranged from 1.3 to 21.8 nmol/L (mean±SD, 6.11±1.45 nmol/L)
in the HHP and from 2.6 to 39.3 nmol/L (mean±SD, 6.04±1.45
nmol/L) in the CHS (excluding 2 outliers with values that were
>4SDs above the median: 2.6 to 20.3 nmol/L). Final statistical
analyses were performed with and without outliers to assess the
effect of their removal on the results. Results were similar, and
bivariate and multivariate analyses are
reported with all values. Similar to our findings in the CHS, PAP
levels increased with age (r0.001) in the HHP. There was
no significant difference in PAP level by sex.
Correlates of PAP
PAP was positively correlated with fibrinogen and CRP (Table 2) but had a negative relationship
with BMI and insulin. There was a positive relationship between PAP and
HDL-C and an inverse correlation with triglycerides. PAP
was not correlated with LDL-C or total cholesterol. There
were also no significant correlations with pack-years of smoking
(former or current smokers), smoking status, hypertensive status,
diabetes, or estrogen use in women (CHS only; data not shown). Major
correlates of PAP did not differ by smoking status (never-smokers
versus former or current smokers; data not shown).
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Both cohorts showed an inverse association between AAI (as a measure of subclinical atherosclerosis) and PAP (significant only in the total CHS group). There were no associations with other markers of subclinical disease, including carotid artery stenosis, the internal or common carotid artery thickness, left ventricular mass, major ECG abnormalities, or a composite subclinical disease variable33 (CHS only; data not shown).
Although there was a suggestion of a stronger relationship between PAP
levels and BHI in the Japanese-American men and a weaker association
with AAI, we were unable to detect an interaction by race. However, the
power of the current study to observe a formal interaction was low. As
expected, PAP, D-dimer, and plasminogen were positively
correlated with each other (Table 3). PAP was also positively
associated with several coagulation factors and thrombin activity. PAP
was negatively associated with PAI-1, probably reflecting the role of
PAI-1 as a fibrinolysis inhibitor (Table 3). In a model to predict PAP, when PAI-1 was included as a
covariate, the relationships of the other individual IRS variables
to PAP were attenuated (Table 4).
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We explored the relationship between PAP and AAI (as a measure of
atherosclerosis) after stratification based on an
increasing number of IRS-related metabolic disorders
(Figure 1). A formal test for
interaction did not reach significance (P=0.26). However,
although all confidence intervals overlapped, PAP appeared more
strongly associated with AAI in the subgroups with 0 or 1
metabolic disorders (P0.001; slope estimate
with [95% confidence interval], -0.14 [-0.20, -0.08]) compared
with the subgroup with 2 or more metabolic disorders
(P=0.10; slope estimate, -0.08 [-0.18, 0.02]) and in
those with NIDDM (P=0.46; slope estimate, -0.04 [-0.15,
0.07]). PAI-1 levels were significantly lower (26 ng/mL) in the
subgroup with 0 or 1 disorder (P0.05) compared with the
other 2 subgroups (2 disorders, 35 ng/mL; NIDDM, 39 ng/mL). Similar
results for PAP were obtained when we stratified the relationship
between PAP and AAI on PAI-1 tertiles (data not shown).
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Multivariate Analyses of PAP in the
CHS
In multivariate analysis, the independent
correlates of PAP were AAI, fasting insulin, BMI, and PAI-1 (all
negative) and fibrinogen, CRP, and factor VIIIc (all positive) (Table 5). With the inflammation factors
(fibrinogen, CRP, and factor VIIIc) removed, AAI was a stronger
correlate. F1+2 and age were associated with AAI in bivariate
analyses and could replace AAI in the final model when we
allowed them to compete for entry.
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Discussion |
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Regarding the correlates of PAP, the major findings in this study are the following: (1) PAP levels were associated with inflammation and procoagulant activity as assessed by factors such as fibrinogen and CRP and F1+2 and fibrinopeptide A, respectively. (2) PAP was negatively associated with factors associated with the IRS (BMI, insulin, and triglycerides), at least partly mediated by PAI-1 levels. (3) PAP was associated with AAI, a measure of subclinical atherosclerosis. (4) In subgroups with 2 or more characteristics of the IRS or in those with NIDDM, the relationship between PAP and AAI appeared weaker than in subgroups with fewer IRS features.
Subclinical Disease
Our study is the first to indicate a relationship between PAP and
a marker of subclinical atherosclerosis, the AAI. This
finding is significant, given the recent report that PAP predicts
incident MI in the elderly, independently of subclinical
disease.9 The lack of relationships with other markers of
subclinical disease, such as carotid artery stenosis or wall
thickness, may be due to the relatively small number of subjects in
this study and the robust nature of AAI as a marker of atherosclerotic
burden.16
Either age or F1+2 could replace AAI in the statistical model. This is not surprising, since we have recently reported in this same cohort that the known age-related increase in AAI was associated with increased thrombin generation.32 Because PAP is correlated with prothrombin fragment F1+2 and age, our results may extend these observations to fibrinolysis status, confirming the relationships observed between subclinical CVD and D-dimer,34 another marker of fibrinolysis.
Inflammation
CVD has features of a chronic inflammatory disease, including mild
elevations of acute-phase proteins (ie, fibrinogen, CRP, and factor
VIIIc), a number of which have been reported to be risk markers for
CVD.1 2 35 36 The independent associations of PAP with
related fibrinolysis factors such as
plasminogen and D-dimer and with inflammation-sensitive
proteins are consistent with a linking role for
fibrinolysis between inflammation and
atherosclerosis. Fibrin degradation products have
been shown to induce the synthesis and release of the proinflammatory
cytokine interleukin-6 from monocytes,37
suggesting a physiological mechanism that conjoins
fibrinolysis and inflammation.
PAI-1 and Components of the IRS
Even though higher levels of PAP and PAI-1, observed in some but
not all studies,3 4 9 38 are both positive predictors of
CVD events, increases in PAI-1 are usually associated with decreases in
PAP.39 Therefore, PAI-1 has been proposed to mediate some
of the adverse hemostatic complications of the IRS.40 Our
findings suggest that the degree of IRS present may modify the
relationship observed between PAP and the risk of CVD events. For
example, a relationship between PAP and CVD may be less obvious in an
obese population or in a population with a high frequency of diabetics.
The apparently stronger association of PAP with AAI when the population
has fewer metabolic disorders supports this position
(Figure 1
). A lowered fibrinolytic response to subclinical
disease in subjects with hyperinsulinemia or
glucose intolerance (and higher PAI-1 levels) is consistent
with the premise that depressed plasmin generation may enhance
progression of atherosclerosis in subjects with
features of the IRS. Our recent case-control study of PAP in the
CHS9 contained too few subjects to explore these results,
and more work is needed to explore these relationships.
Race
The relationships between PAP and hematologic and
metabolic variables were similar in the HHP and the
CHS, confirming the associations we have observed in a second
population. There was a suggestion of a stronger relationship between
BMI and PAP levels in Japanese-American men and a weaker association of
PAP with AAI, CRP, and smoking. Although these differences were
nonsignificant, our power to detect small differences was limited.
The strengths of the current study include the exclusion of clinical CVD as a potential confounder; carefully designed parent studies with appropriate blood collection and storage procedures; assessment of independence through multivariate analyses; and the use of 2 separate racial groups to confirm associations. The major weaknesses are the cross-sectional design and lack of generalizability owing to the selection criteria of the parent studies.
The relationship between inflammation and fibrinolysis is complex, and the biology is incompletely understood. Our results suggest that plasmin generation, as measured by PAP level, is closely associated with ongoing fibrinolysis, subclinical atherosclerosis, and inflammation. Furthermore, PAP levels reflect levels of the major fibrinolysis inhibitor, PAI-1, and increasing levels of PAI-1, as found with the degree of the IRS, may diminish the relationship observed between plasmin generation and atherosclerosis. Thus, insufficient plasmin generation in the setting of a high PAI-1 may be a molecular mechanism for increased thrombosis in subjects with features of the IRS. Taken together with the finding of higher PAP levels as predictors of MI,9 PAP concentration may have different relationships with incident disease, depending on the baseline characteristics of the population studied, ie, presence of the IRS. Larger studies with stratification by IRS characteristics are required to answer these questions.
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Acknowledgments |
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This work was supported by National Heart, Lung, and Blood Institute (NHLBI) contracts NO1-HC-85079 through NO1-HC-85086 (National Institutes of Health, Bethesda, Md); US Public Health Hemostasis Training Grant T-32-HL-07594 (Drs Sakkinen and Cushman); and NHLBI grants RO1-HL-46696 (Dr Tracy) and K08-HL-03618 (Dr Cushman). We are grateful to Drs DeClerck and Collen for providing reagents for fibrinolysis measures and Dr Wai-Lee Wong for providing reagents for measuring lipoprotein (a). Participating institutions and principal staff are as follows: Forsyth County, NC—Bowman Gray School of Medicine of Wake Forest University: Gregory L. Burke, Alan Elster, Walter H. Ettinger, Curt D. Furberg, Edward Haponik, Gerardo Heiss, Dalane Kitzman, H. Sidney Klopfenstein, Margie Lamb, David S. Lefkowitz, Mary F. Lyles, Maurice B. Mittelmark, Cathy Nunn, Ward Riley, Grethe S. Tell, James F. Toole, and Beverly Tucker; Forsyth County, NC—Bowman Gray School of Medicine–EKG Reading Center: Kris Calhoun, Harry Calhoun, Farida Rautaharju, Pentti Rautaharju, and Loralee Robertson; Sacramento County, Calif—University of California, Davis: William Bommer, Charles Bernick, Andrew Duxbury, Mary Haan, Calvin Hirsch, Paul Kellerman, Lawrence Laslett, Marshall Lee, Virginia Poirier, John Robbins, Marc Schenker, and Nema Borhani; Washington County, Md—The Johns Hopkins University: M. Jan Busby-Whitehead, Joyce Chabot, George W. Comstock, Linda P. Fried, Joel G. Hill, Steven J. Kittner, Shiriki Kumanyika, David Levine, Joao A. Lima, Neil R. Powe, Thomas R. Price, Jeff Williamson, Moyses Szklo, and Melvyn Tockman; MRI Reading Center–Washington County, Md–The Johns Hopkins University: R. Nick Bryan, Carolyn C. Meltzer, Douglas Fellows, Melanie Hawkins, Patrice Holtz, Michael Kraut, Grace Lee, Larry Schertz, Earl P. Steinberg, Scott Wells, Linda Wilkins, and Nancy C. Yue; Allegheny County, Pa—University of Pittsburgh: Diane G. Ives, Charles A. Jungreis, Laurie Knepper, Lewis H. Kuller, Elaine Meilahn, Peg Meyer, Roberta Moyer, Anne Newman, Richard Schulz, Vivienne E. Smith, and Sidney K. Wolfson; Echocardiography Reading Center (Baseline)—University of California, Irvine: Hoda Anton-Culver, Julius M. Gardin, Margaret Knoll, Tom Kurosaki, and Nathan Wong; Echocardiography Reading Center (Follow-Up)—Georgetown Medical Center: John Gottdiener, Eva Hausner, Stephen Kraus, Judy Gay, Sue Livengood, Mary Ann Yohe, and Retha Webb; Ultrasound Reading Center—Geisinger Medical Center: Daniel H. O'Leary, Joseph F. Polak, and Laurie Funk; Central Blood Analysis Laboratory—University of Vermont: Edwin Bovill, Elaine Cornell, Mary Cushman, and Russell P. Tracy; Respiratory Sciences—University of Arizona-Tucson: Paul Enright; Coordinating Center—University of Washington, Seattle: Alice Arnold, Annette L. Fitzpatrick, Bonnie K. Lind, Richard A. Kronmal, Bruce M. Psaty, David S. Siscovick, Lynn Shemanski, Lloyd Fisher, Will Longstreth, Patricia W. Wahl, David Yanez, Paula Diehr, and Maryann McBurnie; NHLBI Project Office: Robin Boineau, Teri A. Manolio, Peter J. Savage, and Patricia Smith.
Received April 2, 1998; accepted July 28, 1998.
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24. DeClerck P, Mombaerts P, Holvoet P, De Mol M, Collen D. Fibrinolytic response and fibrin fragment D-dimer levels in patients with deep vein thrombosis. Thromb Haemost. 1987;58:1024–1029.[Medline] [Order article via Infotrieve]
25.
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26.
DeClerck P, Alessi M, Verstrecken M, Kruithof E,
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29.
Macy E, Hayes T, Tracy R. Variability in the
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30.
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Cushman M, Psaty B, Macy E, Bovill E, Cornell E, Kuller
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33.
Kuller L, Shemanski L, Psaty B, Borhani N, Gardin J,
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34. Cushman M, Cornell E, Macy E, Psaty B, Tracy R. Correlates of the fibrin fragment D-dimer, a measure of fibrinolysis, in an elderly cohort free of prevalent cardiovascular disease. Circulation. 1995;92(suppl I):I-624. Abstract.
35.
Tracy R, Lemaitre R, Psaty B, Ives D, Evans R, Cushman
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cardiovascular disease in the elderly: results from the
Cardiovascular Health Study and the Rural Health
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36.
Ridker P, Cushman M, Stampfer M, Tracy R, Hennekens C.
Inflammation, aspirin, and the risk of cardiovascular
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37. Robson S, Shephard E, Kirsch R. Fibrin degradation product D-dimer induces the synthesis and release of biologically active IL-1B, IL-6 and plasminogen activator inhibitors from monocytes in vitro. Br J Haematol. 1994;86:322–326.[Medline] [Order article via Infotrieve]
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fibrinolysis in elderly, obese subjects by moderate
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